Left ventricular hypertrophy (LVH) is one of the most potent risk factors for cardiovascular disease (CVD), including ischemic heart disease, chronic heart failure and CVD death. Common risk factors include hypertension and diabetes while a significant portion of the risk is also determined by genes. On a cellular level, studying cardiomyocytes (CMs) has yielded important insights into disease mechanism. There is now growing evidence suggesting that changes in the composition of the cardiac matrix particularly in diabetes contributes to the disease process in CMs. We propose to examine the role and impact of a 'diabetic' cardiac matrix, its interaction with cardiomyocytes and the role of genetic factors by using human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) as a `patient in a dish' model. Our proposal builds on extensive data which is available as part of the NHLBI HyperGen-LVH study. This includes GWAS and Whole Exome Sequence data. In addition, we have already developed hiPSC-CMs lines from 250 HyperGen participants. In Aim 1, we propose to culture hiPSC-CMs from these individuals on a matrix obtained from decellularized hearts of the db/db mouse to investigate cellular and molecular changes. In Aim 2 we perform expression analysis to determine global expression changes associated with the diabetic matrix followed by a pathway analysis to determine functional networks. Finally, in Aim 3, we perform eQTL analysis to determine single nucleotide polymorphisms (SNPs) associated with the response. Utilizing WES data, we will also perform a combined sequence and expression analysis to identify potential rare variants. Our proposal utilizes existing resources, including genetic and phenotypic data in addition to previously established hiPSC-CMs. Our experiments will provide novel insights into the molecular mechanisms underlying the cardiomyocyte-cardiac matrix interaction, its pathways and genetic factors modulating the response. A better understanding of the role of these interactions and networks can build the basis to develop novel treatment options as well as markers for the identification of individuals at increased risk. This becomes particularly important with an aging population and the increase in prevalence of diabetes.